Overview

Dataset statistics

Number of variables25
Number of observations36992
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 MiB
Average record size in memory200.0 B

Variable types

Text5
Numeric6
Categorical9
DateTime2
Boolean3

Alerts

churn_risk_score is highly overall correlated with points_in_walletHigh correlation
complaint_status is highly overall correlated with past_complaintHigh correlation
offer_application_preference is highly overall correlated with used_special_discountHigh correlation
past_complaint is highly overall correlated with complaint_statusHigh correlation
points_in_wallet is highly overall correlated with churn_risk_scoreHigh correlation
used_special_discount is highly overall correlated with offer_application_preferenceHigh correlation
customer_id has unique valuesUnique
Name has unique valuesUnique
security_no has unique valuesUnique

Reproduction

Analysis started2024-07-01 17:57:26.554131
Analysis finished2024-07-01 17:57:50.500747
Duration23.95 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

customer_id
Text

UNIQUE 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2024-07-01T23:27:50.869983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.271734
Min length20

Characters and Unicode

Total characters1304772
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowfffe4300490044003600300030003800
2nd rowfffe43004900440032003100300035003700
3rd rowfffe4300490044003100390032003600
4th rowfffe43004900440036003000330031003600
5th rowfffe43004900440031003900350030003600
ValueCountFrequency (%)
fffe4300490044003600300030003800 1
 
< 0.1%
fffe43004900440033003700330038003900 1
 
< 0.1%
fffe43004900440032003200350033003200 1
 
< 0.1%
fffe43004900440033003300330032003200 1
 
< 0.1%
fffe4300490044003100390032003600 1
 
< 0.1%
fffe43004900440036003000330031003600 1
 
< 0.1%
fffe43004900440031003900350030003600 1
 
< 0.1%
fffe43004900440036003300320035003300 1
 
< 0.1%
fffe43004900440031003100360037003900 1
 
< 0.1%
fffe4300490044003800300035003800 1
 
< 0.1%
Other values (36982) 36982
> 99.9%
2024-07-01T23:27:51.489160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 592862
45.4%
3 236016
 
18.1%
4 168252
 
12.9%
f 110976
 
8.5%
9 51579
 
4.0%
e 36992
 
2.8%
1 20900
 
1.6%
2 20831
 
1.6%
5 20484
 
1.6%
6 16674
 
1.3%
Other values (2) 29206
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1156804
88.7%
Lowercase Letter 147968
 
11.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 592862
51.2%
3 236016
 
20.4%
4 168252
 
14.5%
9 51579
 
4.5%
1 20900
 
1.8%
2 20831
 
1.8%
5 20484
 
1.8%
6 16674
 
1.4%
8 14641
 
1.3%
7 14565
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
f 110976
75.0%
e 36992
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1156804
88.7%
Latin 147968
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 592862
51.2%
3 236016
 
20.4%
4 168252
 
14.5%
9 51579
 
4.5%
1 20900
 
1.8%
2 20831
 
1.8%
5 20484
 
1.8%
6 16674
 
1.4%
8 14641
 
1.3%
7 14565
 
1.3%
Latin
ValueCountFrequency (%)
f 110976
75.0%
e 36992
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1304772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 592862
45.4%
3 236016
 
18.1%
4 168252
 
12.9%
f 110976
 
8.5%
9 51579
 
4.0%
e 36992
 
2.8%
1 20900
 
1.6%
2 20831
 
1.6%
5 20484
 
1.6%
6 16674
 
1.3%
Other values (2) 29206
 
2.2%

Name
Text

UNIQUE 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2024-07-01T23:27:52.030103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length13.522924
Min length6

Characters and Unicode

Total characters500240
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowPattie Morrisey
2nd rowTraci Peery
3rd rowMerideth Mcmeen
4th rowEufemia Cardwell
5th rowMeghan Kosak
ValueCountFrequency (%)
sidney 137
 
0.2%
gilda 126
 
0.2%
selena 123
 
0.2%
noe 123
 
0.2%
lesli 121
 
0.2%
karri 121
 
0.2%
marietta 120
 
0.2%
kenny 120
 
0.2%
earlie 119
 
0.2%
dean 119
 
0.2%
Other values (2511) 72755
98.3%
2024-07-01T23:27:52.778941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 54599
 
10.9%
a 50451
 
10.1%
36992
 
7.4%
n 36286
 
7.3%
r 34094
 
6.8%
i 33029
 
6.6%
l 29840
 
6.0%
o 24859
 
5.0%
t 17988
 
3.6%
s 15985
 
3.2%
Other values (43) 166117
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 389264
77.8%
Uppercase Letter 73984
 
14.8%
Space Separator 36992
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 54599
14.0%
a 50451
13.0%
n 36286
9.3%
r 34094
8.8%
i 33029
8.5%
l 29840
 
7.7%
o 24859
 
6.4%
t 17988
 
4.6%
s 15985
 
4.1%
d 11744
 
3.0%
Other values (16) 80389
20.7%
Uppercase Letter
ValueCountFrequency (%)
S 7104
 
9.6%
M 6774
 
9.2%
L 5702
 
7.7%
B 4974
 
6.7%
A 4941
 
6.7%
C 4690
 
6.3%
D 3984
 
5.4%
K 3882
 
5.2%
G 3507
 
4.7%
R 3447
 
4.7%
Other values (16) 24979
33.8%
Space Separator
ValueCountFrequency (%)
36992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 463248
92.6%
Common 36992
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 54599
 
11.8%
a 50451
 
10.9%
n 36286
 
7.8%
r 34094
 
7.4%
i 33029
 
7.1%
l 29840
 
6.4%
o 24859
 
5.4%
t 17988
 
3.9%
s 15985
 
3.5%
d 11744
 
2.5%
Other values (42) 154373
33.3%
Common
ValueCountFrequency (%)
36992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 54599
 
10.9%
a 50451
 
10.1%
36992
 
7.4%
n 36286
 
7.3%
r 34094
 
6.8%
i 33029
 
6.6%
l 29840
 
6.0%
o 24859
 
5.0%
t 17988
 
3.6%
s 15985
 
3.2%
Other values (43) 166117
33.2%

age
Real number (ℝ)

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.118161
Minimum10
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2024-07-01T23:27:53.045980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123
median37
Q351
95-th percentile62
Maximum64
Range54
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.867412
Coefficient of variation (CV)0.4274838
Kurtosis-1.1987327
Mean37.118161
Median Absolute Deviation (MAD)14
Skewness-0.0073193193
Sum1373075
Variance251.77477
MonotonicityNot monotonic
2024-07-01T23:27:53.294730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 720
 
1.9%
42 716
 
1.9%
16 716
 
1.9%
38 714
 
1.9%
30 711
 
1.9%
61 709
 
1.9%
60 704
 
1.9%
57 704
 
1.9%
41 699
 
1.9%
59 696
 
1.9%
Other values (45) 29903
80.8%
ValueCountFrequency (%)
10 670
1.8%
11 654
1.8%
12 661
1.8%
13 654
1.8%
14 670
1.8%
15 649
1.8%
16 716
1.9%
17 683
1.8%
18 629
1.7%
19 660
1.8%
ValueCountFrequency (%)
64 672
1.8%
63 656
1.8%
62 677
1.8%
61 709
1.9%
60 704
1.9%
59 696
1.9%
58 678
1.8%
57 704
1.9%
56 682
1.8%
55 695
1.9%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
F
18490 
M
18443 
Unknown
 
59

Length

Max length7
Median length1
Mean length1.0095696
Min length1

Characters and Unicode

Total characters37346
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F 18490
50.0%
M 18443
49.9%
Unknown 59
 
0.2%

Length

2024-07-01T23:27:53.527501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:27:53.720335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 18490
50.0%
m 18443
49.9%
unknown 59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
F 18490
49.5%
M 18443
49.4%
n 177
 
0.5%
U 59
 
0.2%
k 59
 
0.2%
o 59
 
0.2%
w 59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36992
99.1%
Lowercase Letter 354
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 177
50.0%
k 59
 
16.7%
o 59
 
16.7%
w 59
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
F 18490
50.0%
M 18443
49.9%
U 59
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 37346
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 18490
49.5%
M 18443
49.4%
n 177
 
0.5%
U 59
 
0.2%
k 59
 
0.2%
o 59
 
0.2%
w 59
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 18490
49.5%
M 18443
49.4%
n 177
 
0.5%
U 59
 
0.2%
k 59
 
0.2%
o 59
 
0.2%
w 59
 
0.2%

security_no
Text

UNIQUE 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2024-07-01T23:27:54.303227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters258944
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowXW0DQ7H
2nd row5K0N3X1
3rd row1F2TCL3
4th rowVJGJ33N
5th rowSVZXCWB
ValueCountFrequency (%)
xw0dq7h 1
 
< 0.1%
6rz86vw 1
 
< 0.1%
c229qzz 1
 
< 0.1%
0481qnq 1
 
< 0.1%
1f2tcl3 1
 
< 0.1%
vjgj33n 1
 
< 0.1%
svzxcwb 1
 
< 0.1%
psg1lgf 1
 
< 0.1%
r3cx1ea 1
 
< 0.1%
4uj1551 1
 
< 0.1%
Other values (36982) 36982
> 99.9%
2024-07-01T23:27:55.102891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 7410
 
2.9%
D 7391
 
2.9%
H 7346
 
2.8%
Z 7307
 
2.8%
A 7294
 
2.8%
T 7270
 
2.8%
F 7262
 
2.8%
R 7249
 
2.8%
M 7248
 
2.8%
5 7244
 
2.8%
Other values (26) 185923
71.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 187293
72.3%
Decimal Number 71651
 
27.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 7410
 
4.0%
D 7391
 
3.9%
H 7346
 
3.9%
Z 7307
 
3.9%
A 7294
 
3.9%
T 7270
 
3.9%
F 7262
 
3.9%
R 7249
 
3.9%
M 7248
 
3.9%
O 7229
 
3.9%
Other values (16) 114287
61.0%
Decimal Number
ValueCountFrequency (%)
5 7244
10.1%
2 7236
10.1%
7 7220
10.1%
8 7213
10.1%
9 7171
10.0%
4 7171
10.0%
1 7163
10.0%
0 7157
10.0%
3 7043
9.8%
6 7033
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 187293
72.3%
Common 71651
 
27.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 7410
 
4.0%
D 7391
 
3.9%
H 7346
 
3.9%
Z 7307
 
3.9%
A 7294
 
3.9%
T 7270
 
3.9%
F 7262
 
3.9%
R 7249
 
3.9%
M 7248
 
3.9%
O 7229
 
3.9%
Other values (16) 114287
61.0%
Common
ValueCountFrequency (%)
5 7244
10.1%
2 7236
10.1%
7 7220
10.1%
8 7213
10.1%
9 7171
10.0%
4 7171
10.0%
1 7163
10.0%
0 7157
10.0%
3 7043
9.8%
6 7033
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 7410
 
2.9%
D 7391
 
2.9%
H 7346
 
2.8%
Z 7307
 
2.8%
A 7294
 
2.8%
T 7270
 
2.8%
F 7262
 
2.8%
R 7249
 
2.8%
M 7248
 
2.8%
5 7244
 
2.8%
Other values (26) 185923
71.8%

region_category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Town
19556 
City
12737 
Village
4699 

Length

Max length7
Median length4
Mean length4.3810824
Min length4

Characters and Unicode

Total characters162065
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVillage
2nd rowCity
3rd rowTown
4th rowCity
5th rowCity

Common Values

ValueCountFrequency (%)
Town 19556
52.9%
City 12737
34.4%
Village 4699
 
12.7%

Length

2024-07-01T23:27:55.365401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:27:55.603464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
town 19556
52.9%
city 12737
34.4%
village 4699
 
12.7%

Most occurring characters

ValueCountFrequency (%)
T 19556
12.1%
o 19556
12.1%
w 19556
12.1%
n 19556
12.1%
i 17436
10.8%
C 12737
7.9%
t 12737
7.9%
y 12737
7.9%
l 9398
5.8%
V 4699
 
2.9%
Other values (3) 14097
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125073
77.2%
Uppercase Letter 36992
 
22.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 19556
15.6%
w 19556
15.6%
n 19556
15.6%
i 17436
13.9%
t 12737
10.2%
y 12737
10.2%
l 9398
7.5%
a 4699
 
3.8%
g 4699
 
3.8%
e 4699
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
T 19556
52.9%
C 12737
34.4%
V 4699
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 162065
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 19556
12.1%
o 19556
12.1%
w 19556
12.1%
n 19556
12.1%
i 17436
10.8%
C 12737
7.9%
t 12737
7.9%
y 12737
7.9%
l 9398
5.8%
V 4699
 
2.9%
Other values (3) 14097
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 19556
12.1%
o 19556
12.1%
w 19556
12.1%
n 19556
12.1%
i 17436
10.8%
C 12737
7.9%
t 12737
7.9%
y 12737
7.9%
l 9398
5.8%
V 4699
 
2.9%
Other values (3) 14097
8.7%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Basic Membership
7724 
No Membership
7692 
Gold Membership
6795 
Silver Membership
5988 
Premium Membership
4455 

Length

Max length19
Median length17
Mean length15.947043
Min length13

Characters and Unicode

Total characters589913
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlatinum Membership
2nd rowPremium Membership
3rd rowNo Membership
4th rowNo Membership
5th rowNo Membership

Common Values

ValueCountFrequency (%)
Basic Membership 7724
20.9%
No Membership 7692
20.8%
Gold Membership 6795
18.4%
Silver Membership 5988
16.2%
Premium Membership 4455
12.0%
Platinum Membership 4338
11.7%

Length

2024-07-01T23:27:55.848066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:27:56.093162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
membership 36992
50.0%
basic 7724
 
10.4%
no 7692
 
10.4%
gold 6795
 
9.2%
silver 5988
 
8.1%
premium 4455
 
6.0%
platinum 4338
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e 84427
14.3%
i 59497
10.1%
m 50240
8.5%
r 47435
 
8.0%
s 44716
 
7.6%
p 36992
 
6.3%
36992
 
6.3%
M 36992
 
6.3%
b 36992
 
6.3%
h 36992
 
6.3%
Other values (14) 118638
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 478937
81.2%
Uppercase Letter 73984
 
12.5%
Space Separator 36992
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 84427
17.6%
i 59497
12.4%
m 50240
10.5%
r 47435
9.9%
s 44716
9.3%
p 36992
7.7%
b 36992
7.7%
h 36992
7.7%
l 17121
 
3.6%
o 14487
 
3.0%
Other values (7) 50038
10.4%
Uppercase Letter
ValueCountFrequency (%)
M 36992
50.0%
P 8793
 
11.9%
B 7724
 
10.4%
N 7692
 
10.4%
G 6795
 
9.2%
S 5988
 
8.1%
Space Separator
ValueCountFrequency (%)
36992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 552921
93.7%
Common 36992
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 84427
15.3%
i 59497
10.8%
m 50240
9.1%
r 47435
8.6%
s 44716
8.1%
p 36992
 
6.7%
M 36992
 
6.7%
b 36992
 
6.7%
h 36992
 
6.7%
l 17121
 
3.1%
Other values (13) 101517
18.4%
Common
ValueCountFrequency (%)
36992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 589913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 84427
14.3%
i 59497
10.1%
m 50240
8.5%
r 47435
 
8.0%
s 44716
 
7.6%
p 36992
 
6.3%
36992
 
6.3%
M 36992
 
6.3%
b 36992
 
6.3%
h 36992
 
6.3%
Other values (14) 118638
20.1%
Distinct1096
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Minimum2015-01-01 00:00:00
Maximum2017-12-31 00:00:00
2024-07-01T23:27:56.401158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:56.705289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
No
15839 
Yes
15715 
?
5438 

Length

Max length3
Median length2
Mean length2.2778168
Min length1

Characters and Unicode

Total characters84261
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd row?
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 15839
42.8%
Yes 15715
42.5%
? 5438
 
14.7%

Length

2024-07-01T23:27:56.990676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:27:57.227565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 15839
42.8%
yes 15715
42.5%
5438
 
14.7%

Most occurring characters

ValueCountFrequency (%)
N 15839
18.8%
o 15839
18.8%
Y 15715
18.7%
e 15715
18.7%
s 15715
18.7%
? 5438
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47269
56.1%
Uppercase Letter 31554
37.4%
Other Punctuation 5438
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 15839
33.5%
e 15715
33.2%
s 15715
33.2%
Uppercase Letter
ValueCountFrequency (%)
N 15839
50.2%
Y 15715
49.8%
Other Punctuation
ValueCountFrequency (%)
? 5438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78823
93.5%
Common 5438
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 15839
20.1%
o 15839
20.1%
Y 15715
19.9%
e 15715
19.9%
s 15715
19.9%
Common
ValueCountFrequency (%)
? 5438
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 15839
18.8%
o 15839
18.8%
Y 15715
18.7%
e 15715
18.7%
s 15715
18.7%
? 5438
 
6.5%
Distinct11359
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2024-07-01T23:27:57.770606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.9114944
Min length4

Characters and Unicode

Total characters292662
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6801 ?
Unique (%)18.4%

Sample

1st rowxxxxxxxx
2nd rowCID21329
3rd rowCID12313
4th rowCID3793
5th rowxxxxxxxx
ValueCountFrequency (%)
xxxxxxxx 17846
48.2%
cid43705 12
 
< 0.1%
cid3979 11
 
< 0.1%
cid49601 10
 
< 0.1%
cid15792 9
 
< 0.1%
cid23978 9
 
< 0.1%
cid40797 9
 
< 0.1%
cid49598 9
 
< 0.1%
cid62015 9
 
< 0.1%
cid43428 8
 
< 0.1%
Other values (11350) 19065
51.5%
2024-07-01T23:27:58.647239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 142768
48.8%
C 19141
 
6.5%
I 19141
 
6.5%
D 19141
 
6.5%
1 10838
 
3.7%
2 10822
 
3.7%
3 10766
 
3.7%
5 10537
 
3.6%
4 10515
 
3.6%
6 8761
 
3.0%
Other values (12) 30232
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 142813
48.8%
Decimal Number 92416
31.6%
Uppercase Letter 57428
19.6%
Space Separator 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10838
11.7%
2 10822
11.7%
3 10766
11.6%
5 10537
11.4%
4 10515
11.4%
6 8761
9.5%
0 7591
8.2%
8 7559
8.2%
9 7550
8.2%
7 7477
8.1%
Lowercase Letter
ValueCountFrequency (%)
x 142768
> 99.9%
r 15
 
< 0.1%
e 10
 
< 0.1%
o 5
 
< 0.1%
f 5
 
< 0.1%
a 5
 
< 0.1%
l 5
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 19141
33.3%
I 19141
33.3%
D 19141
33.3%
N 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200241
68.4%
Common 92421
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
x 142768
71.3%
C 19141
 
9.6%
I 19141
 
9.6%
D 19141
 
9.6%
r 15
 
< 0.1%
e 10
 
< 0.1%
N 5
 
< 0.1%
o 5
 
< 0.1%
f 5
 
< 0.1%
a 5
 
< 0.1%
Common
ValueCountFrequency (%)
1 10838
11.7%
2 10822
11.7%
3 10766
11.6%
5 10537
11.4%
4 10515
11.4%
6 8761
9.5%
0 7591
8.2%
8 7559
8.2%
9 7550
8.2%
7 7477
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 292662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 142768
48.8%
C 19141
 
6.5%
I 19141
 
6.5%
D 19141
 
6.5%
1 10838
 
3.7%
2 10822
 
3.7%
3 10766
 
3.7%
5 10537
 
3.6%
4 10515
 
3.6%
6 8761
 
3.0%
Other values (12) 30232
 
10.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Gift Vouchers/Coupons
12637 
Credit/Debit Card Offers
12274 
Without Offers
12081 

Length

Max length24
Median length21
Mean length19.709316
Min length14

Characters and Unicode

Total characters729087
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGift Vouchers/Coupons
2nd rowGift Vouchers/Coupons
3rd rowGift Vouchers/Coupons
4th rowGift Vouchers/Coupons
5th rowCredit/Debit Card Offers

Common Values

ValueCountFrequency (%)
Gift Vouchers/Coupons 12637
34.2%
Credit/Debit Card Offers 12274
33.2%
Without Offers 12081
32.7%

Length

2024-07-01T23:27:58.942719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:27:59.179181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
offers 24355
28.2%
gift 12637
14.7%
vouchers/coupons 12637
14.7%
credit/debit 12274
14.2%
card 12274
14.2%
without 12081
14.0%

Most occurring characters

ValueCountFrequency (%)
r 61540
 
8.4%
e 61540
 
8.4%
f 61347
 
8.4%
t 61347
 
8.4%
o 49992
 
6.9%
s 49629
 
6.8%
49266
 
6.8%
i 49266
 
6.8%
u 37355
 
5.1%
C 37185
 
5.1%
Other values (13) 210620
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 543741
74.6%
Uppercase Letter 111169
 
15.2%
Space Separator 49266
 
6.8%
Other Punctuation 24911
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 61540
11.3%
e 61540
11.3%
f 61347
11.3%
t 61347
11.3%
o 49992
9.2%
s 49629
9.1%
i 49266
9.1%
u 37355
6.9%
h 24718
 
4.5%
d 24548
 
4.5%
Other values (5) 62459
11.5%
Uppercase Letter
ValueCountFrequency (%)
C 37185
33.4%
O 24355
21.9%
G 12637
 
11.4%
V 12637
 
11.4%
D 12274
 
11.0%
W 12081
 
10.9%
Space Separator
ValueCountFrequency (%)
49266
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 24911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 654910
89.8%
Common 74177
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 61540
 
9.4%
e 61540
 
9.4%
f 61347
 
9.4%
t 61347
 
9.4%
o 49992
 
7.6%
s 49629
 
7.6%
i 49266
 
7.5%
u 37355
 
5.7%
C 37185
 
5.7%
h 24718
 
3.8%
Other values (11) 160991
24.6%
Common
ValueCountFrequency (%)
49266
66.4%
/ 24911
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 729087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 61540
 
8.4%
e 61540
 
8.4%
f 61347
 
8.4%
t 61347
 
8.4%
o 49992
 
6.9%
s 49629
 
6.8%
49266
 
6.8%
i 49266
 
6.8%
u 37355
 
5.1%
C 37185
 
5.1%
Other values (13) 210620
28.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Desktop
13913 
Smartphone
13876 
?
5393 
Both
3810 

Length

Max length10
Median length7
Mean length6.941609
Min length1

Characters and Unicode

Total characters256784
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowSmartphone

Common Values

ValueCountFrequency (%)
Desktop 13913
37.6%
Smartphone 13876
37.5%
? 5393
 
14.6%
Both 3810
 
10.3%

Length

2024-07-01T23:27:59.447019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:27:59.685225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
desktop 13913
37.6%
smartphone 13876
37.5%
5393
 
14.6%
both 3810
 
10.3%

Most occurring characters

ValueCountFrequency (%)
t 31599
12.3%
o 31599
12.3%
e 27789
10.8%
p 27789
10.8%
h 17686
 
6.9%
D 13913
 
5.4%
s 13913
 
5.4%
k 13913
 
5.4%
S 13876
 
5.4%
m 13876
 
5.4%
Other values (5) 50831
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 219792
85.6%
Uppercase Letter 31599
 
12.3%
Other Punctuation 5393
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 31599
14.4%
o 31599
14.4%
e 27789
12.6%
p 27789
12.6%
h 17686
8.0%
s 13913
6.3%
k 13913
6.3%
m 13876
6.3%
a 13876
6.3%
r 13876
6.3%
Uppercase Letter
ValueCountFrequency (%)
D 13913
44.0%
S 13876
43.9%
B 3810
 
12.1%
Other Punctuation
ValueCountFrequency (%)
? 5393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 251391
97.9%
Common 5393
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 31599
12.6%
o 31599
12.6%
e 27789
11.1%
p 27789
11.1%
h 17686
 
7.0%
D 13913
 
5.5%
s 13913
 
5.5%
k 13913
 
5.5%
S 13876
 
5.5%
m 13876
 
5.5%
Other values (4) 45438
18.1%
Common
ValueCountFrequency (%)
? 5393
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 31599
12.3%
o 31599
12.3%
e 27789
10.8%
p 27789
10.8%
h 17686
 
6.9%
D 13913
 
5.4%
s 13913
 
5.4%
k 13913
 
5.4%
S 13876
 
5.4%
m 13876
 
5.4%
Other values (5) 50831
19.8%

internet_option
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Wi-Fi
12413 
Mobile_Data
12343 
Fiber_Optic
12236 

Length

Max length11
Median length11
Mean length8.9866458
Min length5

Characters and Unicode

Total characters332434
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWi-Fi
2nd rowMobile_Data
3rd rowWi-Fi
4th rowMobile_Data
5th rowMobile_Data

Common Values

ValueCountFrequency (%)
Wi-Fi 12413
33.6%
Mobile_Data 12343
33.4%
Fiber_Optic 12236
33.1%

Length

2024-07-01T23:27:59.940354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:28:00.170012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
wi-fi 12413
33.6%
mobile_data 12343
33.4%
fiber_optic 12236
33.1%

Most occurring characters

ValueCountFrequency (%)
i 61641
18.5%
a 24686
 
7.4%
F 24649
 
7.4%
e 24579
 
7.4%
t 24579
 
7.4%
_ 24579
 
7.4%
b 24579
 
7.4%
W 12413
 
3.7%
- 12413
 
3.7%
l 12343
 
3.7%
Other values (7) 85973
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 221458
66.6%
Uppercase Letter 73984
 
22.3%
Connector Punctuation 24579
 
7.4%
Dash Punctuation 12413
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 61641
27.8%
a 24686
11.1%
e 24579
 
11.1%
t 24579
 
11.1%
b 24579
 
11.1%
l 12343
 
5.6%
o 12343
 
5.6%
r 12236
 
5.5%
p 12236
 
5.5%
c 12236
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F 24649
33.3%
W 12413
16.8%
D 12343
16.7%
M 12343
16.7%
O 12236
16.5%
Connector Punctuation
ValueCountFrequency (%)
_ 24579
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 295442
88.9%
Common 36992
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 61641
20.9%
a 24686
8.4%
F 24649
 
8.3%
e 24579
 
8.3%
t 24579
 
8.3%
b 24579
 
8.3%
W 12413
 
4.2%
l 12343
 
4.2%
o 12343
 
4.2%
D 12343
 
4.2%
Other values (5) 61287
20.7%
Common
ValueCountFrequency (%)
_ 24579
66.4%
- 12413
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 61641
18.5%
a 24686
 
7.4%
F 24649
 
7.4%
e 24579
 
7.4%
t 24579
 
7.4%
_ 24579
 
7.4%
b 24579
 
7.4%
W 12413
 
3.7%
- 12413
 
3.7%
l 12343
 
3.7%
Other values (7) 85973
25.9%
Distinct30101
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Minimum2024-07-01 00:00:00
Maximum2024-07-01 23:59:59
2024-07-01T23:28:00.652482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:28:00.954829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

days_since_last_login
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-41.915576
Minimum-999
Maximum26
Zeros0
Zeros (%)0.0%
Negative1999
Negative (%)5.4%
Memory size289.1 KiB
2024-07-01T23:28:01.222836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q18
median12
Q316
95-th percentile22
Maximum26
Range1025
Interquartile range (IQR)8

Descriptive statistics

Standard deviation228.8199
Coefficient of variation (CV)-5.4590661
Kurtosis13.545985
Mean-41.915576
Median Absolute Deviation (MAD)4
Skewness-3.9413558
Sum-1550541
Variance52358.547
MonotonicityNot monotonic
2024-07-01T23:28:01.465037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12 2380
 
6.4%
13 2373
 
6.4%
14 2307
 
6.2%
15 2278
 
6.2%
11 2262
 
6.1%
10 2091
 
5.7%
16 2068
 
5.6%
-999 1999
 
5.4%
9 1863
 
5.0%
17 1747
 
4.7%
Other values (17) 15624
42.2%
ValueCountFrequency (%)
-999 1999
5.4%
1 328
 
0.9%
2 613
 
1.7%
3 852
2.3%
4 998
2.7%
5 1234
3.3%
6 1257
3.4%
7 1442
3.9%
8 1571
4.2%
9 1863
5.0%
ValueCountFrequency (%)
26 82
 
0.2%
25 203
 
0.5%
24 471
 
1.3%
23 727
2.0%
22 895
2.4%
21 1015
2.7%
20 1184
3.2%
19 1308
3.5%
18 1444
3.9%
17 1747
4.7%

avg_time_spent
Real number (ℝ)

Distinct25961
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.47233
Minimum-2814.1091
Maximum3235.5785
Zeros0
Zeros (%)0.0%
Negative1719
Negative (%)4.6%
Memory size289.1 KiB
2024-07-01T23:28:01.668879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2814.1091
5-th percentile30.15
Q160.1025
median161.765
Q3356.515
95-th percentile1031.0767
Maximum3235.5785
Range6049.6876
Interquartile range (IQR)296.4125

Descriptive statistics

Standard deviation398.28915
Coefficient of variation (CV)1.6358703
Kurtosis5.0039153
Mean243.47233
Median Absolute Deviation (MAD)122.88
Skewness0.53962402
Sum9006528.6
Variance158634.25
MonotonicityNot monotonic
2024-07-01T23:28:01.897953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.1 21
 
0.1%
34.71 20
 
0.1%
33.68 20
 
0.1%
34.33 19
 
0.1%
31.49 18
 
< 0.1%
33.28 18
 
< 0.1%
32.91 18
 
< 0.1%
30.56 18
 
< 0.1%
33.71 18
 
< 0.1%
32.96 17
 
< 0.1%
Other values (25951) 36805
99.5%
ValueCountFrequency (%)
-2814.10911 1
< 0.1%
-2281.236526 1
< 0.1%
-2096.580681 1
< 0.1%
-2093.121606 1
< 0.1%
-2034.80188 1
< 0.1%
-2012.267374 1
< 0.1%
-1960.479169 1
< 0.1%
-1941.035419 1
< 0.1%
-1918.486339 1
< 0.1%
-1913.405154 1
< 0.1%
ValueCountFrequency (%)
3235.578521 1
< 0.1%
3040.41 1
< 0.1%
2899.66 1
< 0.1%
2861.23 1
< 0.1%
2770.56 1
< 0.1%
2766.75 1
< 0.1%
2747.89134 1
< 0.1%
2732.7 1
< 0.1%
2722.077794 1
< 0.1%
2705.756608 1
< 0.1%

avg_transaction_value
Real number (ℝ)

Distinct36894
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29271.194
Minimum800.46
Maximum99914.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2024-07-01T23:28:02.109963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum800.46
5-th percentile3468.9665
Q114177.54
median27554.485
Q340855.11
95-th percentile67338.889
Maximum99914.05
Range99113.59
Interquartile range (IQR)26677.57

Descriptive statistics

Standard deviation19444.806
Coefficient of variation (CV)0.66429836
Kurtosis1.428287
Mean29271.194
Median Absolute Deviation (MAD)13336.775
Skewness1.0110272
Sum1.0828 × 109
Variance3.7810049 × 108
MonotonicityNot monotonic
2024-07-01T23:28:02.349504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14176.97 2
 
< 0.1%
7282.58 2
 
< 0.1%
30126.02 2
 
< 0.1%
21244.03 2
 
< 0.1%
23142.51 2
 
< 0.1%
35460.38 2
 
< 0.1%
9341.33 2
 
< 0.1%
34143.6 2
 
< 0.1%
3432.73 2
 
< 0.1%
6801.07 2
 
< 0.1%
Other values (36884) 36972
99.9%
ValueCountFrequency (%)
800.46 1
< 0.1%
804.34 1
< 0.1%
806.22 1
< 0.1%
806.71 1
< 0.1%
813.82 1
< 0.1%
815.22 1
< 0.1%
821.83 1
< 0.1%
822.7 1
< 0.1%
823.49 1
< 0.1%
823.68 1
< 0.1%
ValueCountFrequency (%)
99914.05 1
< 0.1%
99861.47 1
< 0.1%
99858.02 1
< 0.1%
99819.19 1
< 0.1%
99810.83 1
< 0.1%
99805.52 1
< 0.1%
99803.53 1
< 0.1%
99795.75 1
< 0.1%
99742.63 1
< 0.1%
99730.17 1
< 0.1%
Distinct1654
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2024-07-01T23:28:02.780772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length4.494215
Min length3

Characters and Unicode

Total characters166250
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1623 ?
Unique (%)4.4%

Sample

1st row17.0
2nd row10.0
3rd row22.0
4th row6.0
5th row16.0
ValueCountFrequency (%)
error 3522
 
9.5%
13.0 1394
 
3.8%
19.0 1365
 
3.7%
8.0 1361
 
3.7%
14.0 1355
 
3.7%
17.0 1349
 
3.6%
6.0 1336
 
3.6%
10.0 1334
 
3.6%
18.0 1331
 
3.6%
12.0 1327
 
3.6%
Other values (1644) 21318
57.6%
2024-07-01T23:28:03.438972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36699
22.1%
. 33470
20.1%
1 18719
11.3%
2 16061
9.7%
r 10566
 
6.4%
5 6508
 
3.9%
7 6331
 
3.8%
6 6312
 
3.8%
8 6269
 
3.8%
9 6258
 
3.8%
Other values (5) 19057
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114487
68.9%
Other Punctuation 33470
 
20.1%
Lowercase Letter 14088
 
8.5%
Uppercase Letter 3522
 
2.1%
Dash Punctuation 683
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36699
32.1%
1 18719
16.4%
2 16061
14.0%
5 6508
 
5.7%
7 6331
 
5.5%
6 6312
 
5.5%
8 6269
 
5.5%
9 6258
 
5.5%
3 5692
 
5.0%
4 5638
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
r 10566
75.0%
o 3522
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 33470
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 3522
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148640
89.4%
Latin 17610
 
10.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36699
24.7%
. 33470
22.5%
1 18719
12.6%
2 16061
10.8%
5 6508
 
4.4%
7 6331
 
4.3%
6 6312
 
4.2%
8 6269
 
4.2%
9 6258
 
4.2%
3 5692
 
3.8%
Other values (2) 6321
 
4.3%
Latin
ValueCountFrequency (%)
r 10566
60.0%
E 3522
 
20.0%
o 3522
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36699
22.1%
. 33470
20.1%
1 18719
11.3%
2 16061
9.7%
r 10566
 
6.4%
5 6508
 
3.9%
7 6331
 
3.8%
6 6312
 
3.8%
8 6269
 
3.8%
9 6258
 
3.8%
Other values (5) 19057
11.5%

points_in_wallet
Real number (ℝ)

HIGH CORRELATION 

Distinct23700
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.8822
Minimum-760.66124
Maximum2069.0698
Zeros0
Zeros (%)0.0%
Negative136
Negative (%)0.4%
Memory size289.1 KiB
2024-07-01T23:28:03.685684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-760.66124
5-th percentile351.82615
Q1624.35
median686.8822
Q3757.0025
95-th percentile1028.8753
Maximum2069.0698
Range2829.731
Interquartile range (IQR)132.6525

Descriptive statistics

Standard deviation184.81168
Coefficient of variation (CV)0.26905877
Kurtosis5.1876557
Mean686.8822
Median Absolute Deviation (MAD)66.652199
Skewness-0.08432911
Sum25409146
Variance34155.358
MonotonicityNot monotonic
2024-07-01T23:28:03.925645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
686.8821987 3443
 
9.3%
705.07 9
 
< 0.1%
780.92 8
 
< 0.1%
771.75 7
 
< 0.1%
710.69 7
 
< 0.1%
760.54 7
 
< 0.1%
760.76 6
 
< 0.1%
719.78 6
 
< 0.1%
760.58 6
 
< 0.1%
748.98 6
 
< 0.1%
Other values (23690) 33487
90.5%
ValueCountFrequency (%)
-760.6612363 1
< 0.1%
-549.3574977 1
< 0.1%
-506.2567158 1
< 0.1%
-483.8564006 1
< 0.1%
-471.577009 1
< 0.1%
-469.0203988 1
< 0.1%
-445.2884572 1
< 0.1%
-424.6705248 1
< 0.1%
-412.4416878 1
< 0.1%
-405.2670355 1
< 0.1%
ValueCountFrequency (%)
2069.069761 1
< 0.1%
1816.933696 1
< 0.1%
1780.720173 1
< 0.1%
1763.351594 1
< 0.1%
1759.002532 1
< 0.1%
1755.455512 1
< 0.1%
1755.094693 1
< 0.1%
1751.304195 1
< 0.1%
1750.611562 1
< 0.1%
1736.332594 1
< 0.1%

used_special_discount
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20342 
False
16650 
ValueCountFrequency (%)
True 20342
55.0%
False 16650
45.0%
2024-07-01T23:28:04.127595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

offer_application_preference
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20440 
False
16552 
ValueCountFrequency (%)
True 20440
55.3%
False 16552
44.7%
2024-07-01T23:28:04.297487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

past_complaint
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
18602 
True
18390 
ValueCountFrequency (%)
False 18602
50.3%
True 18390
49.7%
2024-07-01T23:28:04.459904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

complaint_status
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Not Applicable
18602 
Unsolved
4644 
Solved
4619 
Solved in Follow-up
4577 
No Information Available
4550 

Length

Max length24
Median length14
Mean length14.09648
Min length6

Characters and Unicode

Total characters521457
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowSolved
3rd rowSolved in Follow-up
4th rowUnsolved
5th rowSolved

Common Values

ValueCountFrequency (%)
Not Applicable 18602
50.3%
Unsolved 4644
 
12.6%
Solved 4619
 
12.5%
Solved in Follow-up 4577
 
12.4%
No Information Available 4550
 
12.3%

Length

2024-07-01T23:28:04.669924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:28:04.887106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
not 18602
25.2%
applicable 18602
25.2%
solved 9196
12.5%
unsolved 4644
 
6.3%
in 4577
 
6.2%
follow-up 4577
 
6.2%
no 4550
 
6.2%
information 4550
 
6.2%
available 4550
 
6.2%

Most occurring characters

ValueCountFrequency (%)
l 69298
13.3%
o 55246
 
10.6%
p 41781
 
8.0%
e 36992
 
7.1%
36856
 
7.1%
i 32279
 
6.2%
a 32252
 
6.2%
N 23152
 
4.4%
t 23152
 
4.4%
A 23152
 
4.4%
Other values (16) 147297
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 410753
78.8%
Uppercase Letter 69271
 
13.3%
Space Separator 36856
 
7.1%
Dash Punctuation 4577
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 69298
16.9%
o 55246
13.4%
p 41781
10.2%
e 36992
9.0%
i 32279
7.9%
a 32252
7.9%
t 23152
 
5.6%
b 23152
 
5.6%
c 18602
 
4.5%
v 18390
 
4.5%
Other values (8) 59609
14.5%
Uppercase Letter
ValueCountFrequency (%)
N 23152
33.4%
A 23152
33.4%
S 9196
 
13.3%
U 4644
 
6.7%
F 4577
 
6.6%
I 4550
 
6.6%
Space Separator
ValueCountFrequency (%)
36856
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 480024
92.1%
Common 41433
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 69298
14.4%
o 55246
11.5%
p 41781
 
8.7%
e 36992
 
7.7%
i 32279
 
6.7%
a 32252
 
6.7%
N 23152
 
4.8%
t 23152
 
4.8%
A 23152
 
4.8%
b 23152
 
4.8%
Other values (14) 119568
24.9%
Common
ValueCountFrequency (%)
36856
89.0%
- 4577
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 521457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 69298
13.3%
o 55246
 
10.6%
p 41781
 
8.0%
e 36992
 
7.1%
36856
 
7.1%
i 32279
 
6.2%
a 32252
 
6.2%
N 23152
 
4.4%
t 23152
 
4.4%
A 23152
 
4.4%
Other values (16) 147297
28.2%

feedback
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Poor Product Quality
6350 
No reason specified
6290 
Too many ads
6279 
Poor Website
6271 
Poor Customer Service
6252 
Other values (4)
5550 

Length

Max length24
Median length21
Mean length17.355455
Min length12

Characters and Unicode

Total characters642013
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProducts always in Stock
2nd rowQuality Customer Care
3rd rowPoor Website
4th rowPoor Website
5th rowPoor Website

Common Values

ValueCountFrequency (%)
Poor Product Quality 6350
17.2%
No reason specified 6290
17.0%
Too many ads 6279
17.0%
Poor Website 6271
17.0%
Poor Customer Service 6252
16.9%
Reasonable Price 1417
 
3.8%
User Friendly Website 1391
 
3.8%
Products always in Stock 1382
 
3.7%
Quality Customer Care 1360
 
3.7%

Length

2024-07-01T23:28:05.141322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-01T23:28:05.364790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
poor 18873
18.0%
quality 7710
 
7.4%
website 7662
 
7.3%
customer 7612
 
7.3%
product 6350
 
6.1%
reason 6290
 
6.0%
no 6290
 
6.0%
specified 6290
 
6.0%
too 6279
 
6.0%
many 6279
 
6.0%
Other values (11) 25035
23.9%

Most occurring characters

ValueCountFrequency (%)
o 81027
12.6%
67678
 
10.5%
e 62703
 
9.8%
r 52318
 
8.1%
s 39705
 
6.2%
i 38394
 
6.0%
a 33516
 
5.2%
t 32098
 
5.0%
P 28022
 
4.4%
c 23073
 
3.6%
Other values (21) 183479
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 497567
77.5%
Uppercase Letter 76768
 
12.0%
Space Separator 67678
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 81027
16.3%
e 62703
12.6%
r 52318
10.5%
s 39705
8.0%
i 38394
7.7%
a 33516
 
6.7%
t 32098
 
6.5%
c 23073
 
4.6%
u 23054
 
4.6%
d 21692
 
4.4%
Other values (10) 89987
18.1%
Uppercase Letter
ValueCountFrequency (%)
P 28022
36.5%
C 8972
 
11.7%
Q 7710
 
10.0%
W 7662
 
10.0%
S 7634
 
9.9%
N 6290
 
8.2%
T 6279
 
8.2%
R 1417
 
1.8%
U 1391
 
1.8%
F 1391
 
1.8%
Space Separator
ValueCountFrequency (%)
67678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 574335
89.5%
Common 67678
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 81027
14.1%
e 62703
 
10.9%
r 52318
 
9.1%
s 39705
 
6.9%
i 38394
 
6.7%
a 33516
 
5.8%
t 32098
 
5.6%
P 28022
 
4.9%
c 23073
 
4.0%
u 23054
 
4.0%
Other values (20) 160425
27.9%
Common
ValueCountFrequency (%)
67678
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 642013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 81027
12.6%
67678
 
10.5%
e 62703
 
9.8%
r 52318
 
8.1%
s 39705
 
6.2%
i 38394
 
6.0%
a 33516
 
5.2%
t 32098
 
5.0%
P 28022
 
4.4%
c 23073
 
3.6%
Other values (21) 183479
28.6%

churn_risk_score
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4633975
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative1163
Negative (%)3.1%
Memory size289.1 KiB
2024-07-01T23:28:05.585945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4096609
Coefficient of variation (CV)0.40701679
Kurtosis1.299243
Mean3.4633975
Median Absolute Deviation (MAD)1
Skewness-1.1143052
Sum128118
Variance1.9871439
MonotonicityNot monotonic
2024-07-01T23:28:05.773477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 10424
28.2%
4 10185
27.5%
5 9827
26.6%
2 2741
 
7.4%
1 2652
 
7.2%
-1 1163
 
3.1%
ValueCountFrequency (%)
-1 1163
 
3.1%
1 2652
 
7.2%
2 2741
 
7.4%
3 10424
28.2%
4 10185
27.5%
5 9827
26.6%
ValueCountFrequency (%)
5 9827
26.6%
4 10185
27.5%
3 10424
28.2%
2 2741
 
7.4%
1 2652
 
7.2%
-1 1163
 
3.1%

Interactions

2024-07-01T23:27:47.943245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:43.080343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:44.061036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.027551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.987904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:46.950187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:48.114788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:43.263345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:44.214404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.181554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:46.125796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:47.096975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:48.294718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:43.411486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:44.379494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.335427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:46.310307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:47.250952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:48.478098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:43.580863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:44.531676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.493269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:46.472243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:47.416473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:48.890644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:43.733490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:44.686224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.668627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:46.628589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:47.577298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:49.064994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:43.899159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:44.858804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:45.821736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:46.789258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-07-01T23:27:47.761118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-07-01T23:28:05.949891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ageavg_time_spentavg_transaction_valuechurn_risk_scorecomplaint_statusdays_since_last_loginfeedbackgenderinternet_optionjoined_through_referralmedium_of_operationmembership_categoryoffer_application_preferencepast_complaintpoints_in_walletpreferred_offer_typesregion_categoryused_special_discount
age1.0000.003-0.0010.0040.000-0.0030.0060.0000.0140.0030.0080.0000.0000.012-0.0010.0070.0000.000
avg_time_spent0.0031.0000.019-0.0290.002-0.1000.0210.0000.0100.0880.2090.0040.1040.0000.0110.0070.0120.113
avg_transaction_value-0.0010.0191.000-0.2010.000-0.0050.2440.0000.0020.0330.0230.1310.0350.0000.1050.0390.0240.000
churn_risk_score0.004-0.029-0.2011.0000.0000.0160.4410.0000.0000.0470.0270.4130.0500.011-0.5420.0640.0340.006
complaint_status0.0000.0020.0000.0001.000-0.0000.0000.0000.0050.0050.0000.0000.0111.000-0.0030.0070.0000.004
days_since_last_login-0.003-0.100-0.0050.016-0.0001.0000.0120.0080.0000.0080.0000.0000.0000.000-0.0010.0040.0000.000
feedback0.0060.0210.2440.4410.0000.0121.0000.0030.0050.0470.0280.1880.0490.0100.1490.0640.0330.008
gender0.0000.0000.0000.0000.0000.0080.0031.0000.0000.0000.0020.0000.0000.004-0.0030.0060.0000.000
internet_option0.0140.0100.0020.0000.0050.0000.0050.0001.0000.0040.0000.0030.0000.0000.0040.0000.0000.000
joined_through_referral0.0030.0880.0330.0470.0050.0080.0470.0000.0041.0000.0440.0220.0210.000-0.0150.0000.0000.019
medium_of_operation0.0080.2090.0230.0270.0000.0000.0280.0020.0000.0441.0000.0130.0490.005-0.0070.0000.0000.063
membership_category0.0000.0040.1310.4130.0000.0000.1880.0000.0030.0220.0131.0000.0110.0050.2970.0240.0130.004
offer_application_preference0.0000.1040.0350.0500.0110.0000.0490.0000.0000.0210.0490.0111.0000.0050.0100.0000.0000.814
past_complaint0.0120.0000.0000.0111.0000.0000.0100.0040.0000.0000.0050.0050.0051.000-0.0080.0000.0000.005
points_in_wallet-0.0010.0110.105-0.542-0.003-0.0010.149-0.0030.004-0.015-0.0070.2970.010-0.0081.0000.0110.0060.010
preferred_offer_types0.0070.0070.0390.0640.0070.0040.0640.0060.0000.0000.0000.0240.0000.0000.0111.0000.0040.000
region_category0.0000.0120.0240.0340.0000.0000.0330.0000.0000.0000.0000.0130.0000.0000.0060.0041.0000.000
used_special_discount0.0000.1130.0000.0060.0040.0000.0080.0000.0000.0190.0630.0040.8140.0050.0100.0000.0001.000

Missing values

2024-07-01T23:27:49.377298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-01T23:27:50.045785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idNameagegendersecurity_noregion_categorymembership_categoryjoining_datejoined_through_referralreferral_idpreferred_offer_typesmedium_of_operationinternet_optionlast_visit_timedays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintcomplaint_statusfeedbackchurn_risk_score
0fffe4300490044003600300030003800Pattie Morrisey18FXW0DQ7HVillagePlatinum Membership2017-08-17NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi16:08:0217300.6353005.2517.0781.750000YesYesNoNot ApplicableProducts always in Stock2
1fffe43004900440032003100300035003700Traci Peery32F5K0N3X1CityPremium Membership2017-08-28?CID21329Gift Vouchers/CouponsDesktopMobile_Data12:38:1316306.3412838.3810.0686.882199YesNoYesSolvedQuality Customer Care1
2fffe4300490044003100390032003600Merideth Mcmeen44F1F2TCL3TownNo Membership2016-11-11YesCID12313Gift Vouchers/CouponsDesktopWi-Fi22:53:2114516.1621027.0022.0500.690000NoYesYesSolved in Follow-upPoor Website5
3fffe43004900440036003000330031003600Eufemia Cardwell37MVJGJ33NCityNo Membership2016-10-29YesCID3793Gift Vouchers/CouponsDesktopMobile_Data15:57:501153.2725239.566.0567.660000NoYesYesUnsolvedPoor Website5
4fffe43004900440031003900350030003600Meghan Kosak31FSVZXCWBCityNo Membership2017-09-12NoxxxxxxxxCredit/Debit Card OffersSmartphoneMobile_Data15:46:4420113.1324483.6616.0663.060000NoYesYesSolvedPoor Website5
5fffe43004900440036003300320035003300Leslie Browder13MPSG1LGFCityGold Membership2016-01-08NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi06:46:0723433.6213884.7724.0722.270000YesNoYesUnsolvedNo reason specified3
6fffe43004900440031003100360037003900Bridget Balog21MR3CX1EATownGold Membership2015-03-19YesCID24708Gift Vouchers/CouponsDesktopMobile_Data11:40:041055.388982.5028.0756.210000YesNoYesSolved in Follow-upNo reason specified3
7fffe4300490044003800300035003800Herma Torgeson42M4UJ1551TownNo Membership2016-07-12?CID56614Credit/Debit Card OffersBothFiber_Optic07:52:4319429.1144554.8224.0568.080000NoYesYesUnsolvedPoor Product Quality5
8fffe43004900440033003300330032003200Pattie Helmers44M0481QNQVillageSilver Membership2016-12-14NoxxxxxxxxWithout OffersSmartphoneFiber_Optic06:50:1015191.0718362.3120.0686.882199YesNoYesSolved in Follow-upPoor Customer Service3
9fffe43004900440032003000340038003300Shaquana Leech45FZHP4MCRTownNo Membership2016-11-30NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi19:10:161097.3119244.1628.0706.230000NoYesYesNo Information AvailablePoor Customer Service4
customer_idNameagegendersecurity_noregion_categorymembership_categoryjoining_datejoined_through_referralreferral_idpreferred_offer_typesmedium_of_operationinternet_optionlast_visit_timedays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintcomplaint_statusfeedbackchurn_risk_score
36982fffe43004900440033003600330033003800Leslie Bruneau45FI2TAL7NTownPremium Membership2016-08-31NoxxxxxxxxGift Vouchers/Coupons?Wi-Fi08:30:411034.93000041558.9319.0703.030000YesNoNoNot ApplicablePoor Product Quality3
36983fffe43004900440032003300370030003700Faustina Balog45MPU0XLQYTownBasic Membership2016-08-30YesCID45477Without OffersSmartphoneWi-Fi10:53:31949.33000045358.4911.0242.979625YesNoNoNot ApplicablePoor Customer Service5
36984fffe43004900440035003800320035003300Hilary Ortego51MLM92BDSTownGold Membership2016-10-07NoxxxxxxxxWithout OffersDesktopFiber_Optic15:41:3624312.33000063446.712.0778.700000NoYesNoNot ApplicableProducts always in Stock1
36985fffe4300490044003800310034003500Dwain Cann12FGWAHGJYVillagePremium Membership2016-10-25NoxxxxxxxxGift Vouchers/CouponsDesktopFiber_Optic03:30:1713418.38000056397.217.0725.890000YesYesYesUnsolvedProducts always in Stock2
36986fffe43004900440034003900300036003500Marlena Chastain27M8X0LUUSTownPlatinum Membership2015-09-07YesCID15800Credit/Debit Card OffersDesktopMobile_Data05:29:1913135.8300008225.6816.0748.570000YesNoNoNot ApplicableNo reason specified3
36987fffe43004900440035003500390036003100Cuc Tarr46F6F51HFOTownBasic Membership2017-09-21NoxxxxxxxxCredit/Debit Card OffersDesktopWi-Fi04:14:052-650.68275927277.686.0639.510000NoYesYesNo Information AvailableNo reason specified4
36988fffe43004900440033003500380036003600Jenni Stronach29F21KSM8YTownBasic Membership2016-06-27NoxxxxxxxxWithout OffersSmartphoneWi-Fi23:18:3113-638.12342111069.7128.0527.990000YesNoNoNot ApplicablePoor Customer Service5
36989fffe4300490044003500330034003100Luciana Kinch23FXK1IM9HTownBasic Membership2016-09-11YesCID3838Gift Vouchers/CouponsDesktopWi-Fi03:50:2512154.94000038127.56Error680.470000NoYesYesUnsolvedPoor Website4
36990fffe43004900440031003200390039003000Tawana Ardoin53MK6VTP1ZVillagePlatinum Membership2017-06-15NoxxxxxxxxGift Vouchers/CouponsSmartphoneMobile_Data09:50:0315482.6100002378.8620.0197.264414YesYesNoNot ApplicableNo reason specified3
36991fffe43004900440033003600340034003200Verlene Beaulieu35MLBX0GLRTownSilver Membership2015-10-23NoxxxxxxxxGift Vouchers/CouponsDesktopMobile_Data01:39:521579.1800002189.68Error719.970000YesNoNoNot ApplicableQuality Customer Care2